Read 8 reviews of this seminar.
• Test complex causal theories with multiple pathways.
• Estimate simultaneous equations with reciprocal effects.
• Incorporate latent variables with multiple indicators.
• Investigate mediation and moderation in a systematic way.
• Handle missing data by maximum likelihood (better than multiple imputation).
• Analyze longitudinal data.
• Estimate fixed and random effects models in a comprehensive framework.
• Adjust for measurement error in predictor variables.
Because SEM is such a complex and wide-ranging methodology, learning how to use it can take a substantial investment of time and effort. Now, you have a unique opportunity to learn the basics of SEM from a master teacher, Professor Paul D. Allison, in just two days.
This course is designed for researchers with a moderate statistical background who want to apply SEM methods in their own research projects. No previous background in SEM is necessary. But participants should have a good working knowledge of basic principles of statistical inference (e.g., standard errors, hypothesis tests, confidence intervals), and should also have a good understanding of the basic theory and practice of linear regression.
Mplus will be used for all the empirical examples and exercises in this course. Mplus is one of the best SEM packages because of its superior capabilities for missing data, multi-level modeling, and ordinal and categorical data. Although not required, participants are welcome to bring their own laptop computers to the class.
The class will meet from 9 to 4 each day with a 1-hour lunch break.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
The fee of $795 includes all course materials.
Participants must make their own arrangements for lodging. For more information, click on the
Lodging tab at the top of this page.
1. Introduction to SEM
2. Path analysis
3. Direct and indirect effects
4. Identification problem in nonrecursive models
5. Reliability and validity
6. Multiple indicators of latent variables
7. Exploratory factor analysis
8. Confirmatory factor analysis
9. Goodness of fit measures
10. Structural relations among latent variables
11. Alternative estimation methods.
12. Multiple group analysis
13. Models for ordinal and nominal data
Of those who attended the November session, 87 percent rated the course "very good" or "excellent". Here are some of the comments they made:
“This is another wonderful learning experience from Dr. Allison. After the course, you will be able to use the methods you need.”
ManSoo Yu, University of Missouri
“This was an excellent course, pitch-perfect for applied researchers with moderate statistical background. Dr. Allison provides the perfect mix of theory and application.”
Jeffrey Sonis, University of North Carolina
“As someone who has not had a course in SEM, but only read about it, I found the course to be very informative and clear. Paul takes you from the basics, including links to other similar procedures, through more complex models that reflect real-world applications. He demonstrates the software very well, explains how to interpret and apply results, and answers questions clearly and well. I would recommend him for anyone who is new to SEM, or who wants a refresher.”
Rick Stuetzle, University of Miami
“Dr. Allison clearly explained the statistics behind structural equation modeling and went through different examples of Mplus output in a step-by-step, easy to follow manner. I’d recommend this course for professionals and students who are already somewhat familiar with SEM but who want more in-depth knowledge so they can better navigate through analysis of data.”
“What I most benefitted from in this course was the discussion of congeneric measures and tau-equivalent measures. I think that too often measures are assumed to have an equal level of error variance – which is highly problematic. Dr. Allison makes a strong case for explaining this phenomenon across all types of research and shows that this approach is a stepping stool to adequately understanding the phenomenon of interest.”
Melissa Kimber, St. Michael’s Hospital, Toronto
“This course served as an excellent refresher after having taken an SEM course previously. It helped to clarify concepts, review important ideas, and to hear material presented in a new way. New material was explained well and course materials will be a valuable reference in the future. Thanks very much!”
Courtney Kelsch, University of Miami
“This course provided me with the knowledge necessary to accurately interpret Mplus output and have greater confidence in the conclusions I draw from structural equations models.”
Chad Burton, University of Pittsburgh
“This course will get you started in Mplus.”
Abraham Salinas, University of South Florida
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